{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from collections import Counter\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "domain_names = {\n",
        "    'github': 'GitHub',\n",
        "    'arxiv': 'arXiv',\n",
        "    'c4': 'C4',\n",
        "    'book': 'Books',\n",
        "    'wikipedia': 'Wikipedia',\n",
        "    'tulu': \"Tulu-v3.1\",\n",
        "}\n",
        "\n",
        "def load_data(input_path):\n",
        "    with open(input_path, 'rb') as f:\n",
        "        data = pickle.load(f)\n",
        "    return data\n",
        "\n",
        "def load_data_for_model(model):\n",
        "    df = []\n",
        "    for domain in domain_names.keys():\n",
        "        input_path = f\"routing_output/{model}/expert_counts/{domain}.pkl\"\n",
        "        layer0, layer7, layer15 = load_data(input_path)\n",
        "        counters = {\n",
        "            0: layer0,\n",
        "            7: layer7,\n",
        "            15: layer15,\n",
        "        }\n",
        "        for i in [0, 7, 15]:\n",
        "            total_count = sum(counters[i].values())\n",
        "            for j, v in counters[i].items():\n",
        "                df.append({\n",
        "                    'Domain': domain_names[domain],\n",
        "                    'Layer': i,\n",
        "                    'Expert': j,\n",
        "                    'Proportion': v / total_count * 100,\n",
        "                })\n",
        "    df = pd.DataFrame(df)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1280x880 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2426.75x1250 with 15 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = load_data_for_model('mistral')\n",
        "num_experts = df.Expert.nunique()\n",
        "k = 2\n",
        "df['Proportion'] *= k\n",
        "uniform_proportion = 1 / num_experts * k * 100\n",
        "\n",
        "plt.style.use('seaborn-v0_8-poster')\n",
        "plt.style.use('seaborn-v0_8-darkgrid')\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "sns.despine()\n",
        "\n",
        "COLORS = {\n",
        "    'GitHub': '#F0539B',\n",
        "    'arXiv': '#43C5E0',\n",
        "    'Wikipedia': '#B11BE8',\n",
        "    'Books': '#FDBE15',\n",
        "    'C4': '#2E3168',\n",
        "}\n",
        "\n",
        "FONTSIZE=35\n",
        "LABEL_FREQ = 2\n",
        "TICK_FREQ = 1\n",
        "PADDING = num_experts * 1.0\n",
        "PADDING2 = num_experts * 0.07\n",
        "\n",
        "g = sns.catplot(data=df, x='Expert', y='Proportion', row='Domain', hue=\"Domain\", col=\"Layer\", kind='bar',\n",
        "                row_order=COLORS.keys(), hue_order=COLORS.keys(), palette=COLORS.values(), order=range(num_experts),\n",
        "                aspect=3, height=2.5, gap=0.0, linewidth=0.5, width=1.0, margin_titles=True, sharex=False, saturation=0.9)\n",
        "\n",
        "\n",
        "labels = sum(([i]+[\"\"]*(LABEL_FREQ - 1) for i in range(0, num_experts, LABEL_FREQ)), [])\n",
        "g.set_xticklabels(labels)\n",
        "\n",
        "for rows in g.axes:\n",
        "    for ax in rows:\n",
        "        ax.axhline(y=uniform_proportion, color='grey', linestyle='--' , linewidth=3.9, alpha=0.7)\n",
        "        ax.grid(True, axis='y', linestyle='-', color='#FFE5B4', linewidth=2.5, alpha=0.7)\n",
        "        ax.tick_params(axis='x', which='major', length=4, width=1, color='black', direction='out', reset=True, bottom=True, top=False, labelsize=FONTSIZE)\n",
        "        ax.tick_params(axis='y', which='major', labelsize=FONTSIZE)\n",
        "\n",
        "        ticks = ax.get_xticks()\n",
        "        ticks = [t for t in ticks if t % TICK_FREQ == 0]\n",
        "        ax.set_xticks(ticks)\n",
        "        ax.set_ylim(0, 100)\n",
        "\n",
        "    # Create a bit of padding\n",
        "    ax = rows[0]\n",
        "    ax.set_xlim(ax.get_xlim()[0]-PADDING/2, ax.get_xlim()[1]+PADDING/2 + PADDING2)\n",
        "    ax = rows[1]\n",
        "    ax.set_xlim(ax.get_xlim()[0]-PADDING/2 - PADDING2/2, ax.get_xlim()[1]+PADDING/2 + PADDING2/2)\n",
        "    ax = rows[2]\n",
        "    ax.set_xlim(ax.get_xlim()[0]-PADDING/2 - PADDING2, ax.get_xlim()[1]+PADDING/2)\n",
        "\n",
        "# Remove ticks for all but the bottom row\n",
        "for rows in g.axes[:-1]:\n",
        "    for ax in rows:\n",
        "        ax.set_xticklabels([])\n",
        "\n",
        "g.set_ylabels(\"\")\n",
        "g.set_xlabels(\"\")\n",
        "plt.subplots_adjust(hspace=0.6, wspace=0.0)\n",
        "g.figure.supylabel('Domain specialization (%)', fontsize=FONTSIZE, fontweight='bold', x=-0.025, y=0.55)\n",
        "g.figure.supxlabel('Expert ID', fontsize=FONTSIZE, fontweight='bold', y=-0.03, x=0.46)\n",
        "\n",
        "g.set_titles(row_template=\"\", col_template=\"Layer {col_name}\", size=FONTSIZE, fontweight=\"bold\")\n",
        "g.legend.set_title(\"\")\n",
        "g.legend.set_visible(False)\n",
        "\n",
        "plt.savefig('routing_mixtral_v2.pdf', bbox_inches='tight')\n",
        "plt.savefig('routing_mixtral_v2.jpg', bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1280x880 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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/2OR21113Xfztb39L6/vhD3+Y2Fut5WrDDTeMXXbZJfbZZ5+SVzm+//77ae1M1Z2zZ8+O8ePHx+TJk2PhwoWxyiqrxOqrrx7bbbddTn9V0V41fL5gRMSWW27Z7HazZ8+Oc889t77dvXv3uPjii0t2MppkixYtitNOOy2+/PLL+r6ysrL4yU9+0mL7fPDBByOVSqX15fI+tbZJkybFpZdeWt9eddVV4/zzzy9qzE8++SStXeiJ4lprrZV2YpPE+aYlXr+IiNVWWy122223tNtCP/HEE3HZZZfFaaed1uQtJ++///646aab0vr22GOPWH/99YuOqRQ++OCDtMr6/v37x8CBA2PZsmXx2GOPxb/+9a948803Y8aMGVFTUxMDBgyItdZaK3bYYYfYfffdY8iQIc3uw/xBQ3Lg4smBG5MHtw15cH7kwE2TAxdHDpwfOTBtRR5cHDlwY3LgtiEHzo8cODt5cHHkwfmRB7cfilxItIbPABw0aFBB4wwYMCDtxGbl/3cGv/nNb+LQQw+N6urq+r4xY8bEU089FYcddlhsueWW0bdv35g/f368+uqrcfvtt8eLL76YNsbWW28dp5xySmuHXnIt+TM0rHxebbXV6v//2muvxfXXXx9PP/10o9vgRXz1zL2tt946Tj/99Lxvi5d0n332WYwbNy6tb5NNNml0O7tMfvnLX6YdB0477bQO/5c3S5YsiUceeSSuvfbaRp+pY489Nqfb/RVi/vz5ceutt6b19evXL7baaqsW2V+hli9fHqNGjUp7Ru2YMWOif//+RY1byvlmZUmbb1rq9Vth9OjR8corr6S9njfeeGO8/PLLccQRR8TWW28d/fr1i8WLF8eECRPiL3/5Szz22GNpY6y33nrxq1/9qiTxlELD21Out956ceutt8bvf//7mDt3bqP1p06dGlOnTo2XXnoprrrqqthvv/3itNNOy/rXMOYPGpIDl4YcOJ08uPXJg3MnB85ODlwcOXD+5MC0FXlw8eTA6eTArU8OnDs5cPPkwcWRB+dPHtx+KHIh0RpONIVWjzfcruGzBju6jTfeOG666aY4+eST054z+vTTT8fTTz+ddduysrL4wQ9+EOecc05069atpUNt1xomMAMGDIhly5bFhRdeGHfffXfWbevq6uKll16KQw45JH70ox/F6NGjO0SFeiqVil/84hexbNmytP6DDjqo2W3vv//++Mc//lHf/vrXvx5HHHFEyWNsa9XV1XH55ZfH0qVL49NPP4033ngjFi9e3Gi9o48+Ok477bQWi+PCCy+MWbNmpfUdcMABifscXn/99TFhwoT69v777x+77rpr0eN2lvmmpV6/FdZYY4249dZb4/jjj09L1l977bV47bXXmt1+t912i9/85jdRVVVVspiK1fDEZvz48TF+/Pictq2trY1x48bFM888E9ddd12Tt540f9BQZzkmtTQ5cOtxHGtMHpydHDg/cuDiyIHzJwemrXSW41JLkgO3HsexxuTA2cmB8ycPLo48OH/y4PZDkQuJtmjRorR27969CxqnV69eae2FCxcWHFN7tc0228TDDz8cY8eOjfvuuy9j8tTQkCFD4vLLL49NNtmkFSJs35YvXx4LFixI6ysvL48TTzyx2RPIhu64446YMmVKXHPNNdG1a9dShtnqrrjiinj++efT+tZaa604+OCDs243derU+M1vflPf7tWrV1x44YVN3uKuPfv888/j9ttvb3L5+uuvH2effXbssMMOLRbDPffc0+gvLPr06dOit8QsxIQJE+L3v/99fXv11VdPu4VpMTrDfNOSr9/Khg4dGg899FDcfPPNcffdd8ecOXOa3WbgwIFx6aWXJvJ53++9916Ty3r06BHDhw+PNddcM7p16xYzZsyIV199tdGFgunTp8ePfvSjuPPOO2OzzTZLW2b+IJPOcExqLXLgluc4lpk8ODs5cO7kwMWRAxdGDkxb6QzHpdYgB255jmOZyYGzkwPnRx5cHHlwYeTB7YciFxKtYcVvoc/N7NIl/aNeU1NTcEzt2fz58yOVSkX37t1zOrmZPHlynHTSSXHyySfHfvvt1+GSylJqOClFRPzud7+LZ555pr69ySabxOGHHx7bbrttDBo0KBYuXBjvv/9+PPTQQ/HAAw+kPefvn//8Z1x88cUtknS0lnvuuSduuOGGtL6ysrI4//zzs/41SCqVirPOOistIRw9enSstdZaLRZrW5o6dWrW5Z9++mn84Q9/iNra2thpp51Kvv9//etfMWbMmEb9o0aNilVXXbXk+yvUkiVLYtSoUbF8+fKI+OqzdOGFF0afPn1KMn5LzTcNx20rLf36NbRw4cJYtmxZ9OjRI6cTmxkzZsQZZ5wRxx57bBx++OGNXse2lOnEpmfPnnHKKafEQQcd1Og1TKVS8eSTT8all14akydPru+vrq6OE088McaNG5f23TJ/kIkcuLTkwC3LcawxeXDz5MC5kQMXRw5cODkwbUUeXDpy4JblONaYHLh5cuDcyYOLIw8unDy4/UjOpwYyaHgCUuiBrrOf2NTW1sZVV10Vt9xyS/2kFvHVxLbxxhvHsGHDok+fPjFnzpx466234uOPP65f5/PPP4+zzjor/v73v8dll11WcEVrR5cpeVkxKZWVlcUpp5wSP/3pT6O8vLx+ef/+/WO77baL7bbbLg455JA4/vjj0yo+77jjjthpp51ixx13bPkfoMTuv//++OUvf9mo/yc/+Umzlei33nprvPTSS/Xtb3/7281W+7dnzZ3c1NTUxLPPPhvPPvts7LbbbnHRRReV7Hv47LPPxkknndTomLjXXnvFD37wg5Lso1R+97vfxaRJk+rbhx12WHzzm98s2fgNX4NCb/HXsGJ65WNuW2rp129lt912W1x++eVpz3qN+OqvwjbbbLOoqqqK+fPnx7vvvpt20jBz5sy48MIL4//+7/9i7NixBT8Lt5S++OKLRrcZXWeddeKGG26IIUOGZNymrKwsdtlll9h2223jpJNOSvsLpi+//DKuvvrqOP/88+v7zB9kIgcuDTlw63AcSycPzo0cODdy4OLIgQsjB6YtyYOLJwduHY5j6eTAuZED504eXBx5cGHkwe2LIhcSrby8PK0ira6urqBxGm6XpKrAllZXVxennnpqPPbYY2n93/nOd2LUqFEZD8wTJkyIiy++OF5++eX6vqeeeiqOO+64uPXWWwuuau3IVv6cNnT66afHsccem3X7LbfcMv7whz/ED37wg1i6dGl9/zXXXNPuJqa77747fv3rX0cqlUrr32WXXZp9lugHH3wQl19+eX27qqoq7VaVHdFmm20Wt956awwdOjRWWWWVWLBgQbz99tvx8MMPx0MPPZSWGP/jH/+I2bNnxx//+Mein438+OOPx2mnndYoqdpiiy3iggsuKGrsUnvuuefizjvvrG+vs846ceaZZ5Z0HysnjRHR6PObq4bHgiTMN63x+q1wwQUXxG233ZbWN3LkyBg9enTGZ5BOmjQpLrvssnj88cfr+95888340Y9+FPfcc0+sssoqLRJnrsrLy+ufKTtlypSYPXt2XH/99U2e1Kysd+/ecc0118SBBx4Yn376aX3/fffdFz/96U9jzTXXjAjzB5nJgYsnB249jmP/IQ/OnRy4eXLg4siBCycHpi3Jg4sjB249jmP/IQfOnRw4N/Lg4siDCycPbl/Km18F2k7DCaHQKsiGB43OlJxfd911jU5sjj/++Pj973/f5IF58803j9tuuy0OPfTQtP5XXnklrrzyypYKtV1rqtJ35MiRzU5KK2y00Ubx05/+NK3vjTfeiLfeeqvo+FrLtddeG+eff36jiwnbbbddXHHFFY2Sx5XV1NTEqFGj0pLt8847LwYOHNhi8SbBZpttFt/4xjdiwIAB0bVr1+jfv3/suOOOceGFF8a9994bgwcPTlv/lVdeiauvvrqofd53331x8sknpyVBERHDhg2Lm266KXr06FHU+KU0f/78GD16dP2JRnl5eVx00UUlj7Gjzjet9fpFRDzwwAONTmoOPPDAuP322zOe1ERErLfeejF27NhGFz0+/vjjjH8B1NpWW221OPXUU+Oyyy6Lv/zlL/GPf/wj1ltvvZy379OnT/z85z9P66upqUk7kTN/kElHPSa1Jjlw63Ec+4o8OD9y4OzkwMWRAxdHDkxb6qjHpdYiB249jmNfkQPnRw7cPHlwceTBxZEHty+KXEi0hgfehre7ylV1dXVau60nmtYybdq0Rs/B3GuvveLUU09tdtuKior45S9/Gd/61rfS+m+//fZmb6vXGTW8Ld0Kxx13XF7j/PCHP2w01nPPPVdwXK2lpqYmzjnnnLjmmmsaLdt+++3jhhtuaLbi/Nprr42JEyfWt/faa6/Ya6+9Sh5re7LJJpvErbfeGlVVVWn9t912W8ycOTPv8VKpVFx99dVx9tlnN0rAN95447jtttsa7autnX/++fHll1/Wt4855pjYaqutSr6fjjrftNbrt3Tp0rj44ovT+rbaaqv49a9/ndNfMPz0pz+NQw45JK3vkUceiddee62kcbaFPfbYo9H36tlnn63/f2efP8isox6TWoscuHV19uOYPLj05MBy4GLJgdueHJhCddTjUmuQA7euzn4ckwOXnhz4K/Lg4siD2548uPUociHRGh4IFi5cWNA4ixYtSmv369ev0JDalfvvvz+tErqysjLOOuusnLcvLy+Pc889N21SqqmpiXHjxpU0zo4g07MxKysrY7vttstrnKqqqth0003T+l5//fViQmtx8+bNi2OOOSb++te/Nlq26667xvXXXx/du3fPOsZrr70WN910U3174MCBcd5555U81vboa1/7WqMLEjU1NfG3v/0tr3GWLl0ap59+eowdO7bRshEjRsRtt90W/fv3LybUknv44YfTfs5hw4bFySef3CL7ajgvdIT5pjVfv8ceeyxmz56d1jd69Oi8btF5xhlnRN++fdP67rnnnpLE15bKy8tj5MiRaX2TJ0+u/39nnj9omhy4OHLg1tWZj2Py4JYjB5YDF0oOnAxyYAolDy6cHLh1debjmBy45XTmHDhCHlwseXAyyINbjyIXEm3AgAFp7YYHzVzNmjUr67gd1fPPP5/W3m677WL11VfPa4x11lkntt5667S+F154oejYOprKysro06dPWt/gwYObTegzGTp0aFq74ec3ST7++OM45JBD4sUXX2y07KCDDoqrr7662erlxYsXx1lnnZVWUf6b3/wmkZXkbeWggw6Knj17pvW9+uqrOW8/c+bMOOKII+L//u//Gi371re+FX/84x/b/HmXDU2fPj3OP//8+nbXrl3jkksuabFq+FVXXTWtXeh803C7tppvWvv1azjfrL/++rHFFlvkNUbfvn1jt912S+vrKPPNimeurrDy56Szzh9kJwcujhy4dXXW45g8uOXJgeXA+ZIDJ4scmELIgwsnB25dnfU4JgdueZ0xB46QBxdLHpws8uDWkXtJFbSBwYMHx/jx4+vb06ZNK2ic6dOnp7XzTfDbqw8//DCtPXz48ILG2XrrrdMmlw8++KCYsDqsNdZYIxYsWFDfbjhR5aph9ercuXOLCavFvPzyy3HSSSdljO+kk06Kn/3sZzmNM2HChPjkk0/S+ho+TzAfu+yyS1p78ODB8eSTTxY8XhJUVlbG5ptvnnYCOWXKlJy2/eCDD+KnP/1pfP75542WHXzwwfGrX/0qrwrr1vLMM8+kfbZqampi//33L3i8DTfcMK399a9/Pe644476dsNn3pZqvllttdUKGqdYrf36lXK+ue++++rbU6dOjUWLFkWvXr0KGi8pGsbf8BaonW3+oHly4OLIgVtfZzuOyYNbhxxYDpwvOXCyyIEphDy4cHLg1tfZjmNy4NbRGXPgCHlwseTBySIPbh3u5EKiDRkyJK3dMPnJxYwZM2Lx4sVpfRtssEFRcbUX8+bNS2sXWg3dsPp0/vz5hYbUoTX8vM6ZM6egcVa+tWhE2z/HMZPHHnssjj766EaTZteuXeOCCy7I+aSG3DX8Hja8FWIm48ePjx/+8IcZT2xOOeWU+M1vfpPYE5vW1vD7+9lnn0UqlcprjFQqFZ9++mlaX8Nq6o6qpeabiI4x56x80hLR+PXpTPMHuZEDF0cO3Po603FMHty65MAtSw5cHDlwdnJgCiEPLpwcuPV1puOYHLh1yYFbnjy4OPLg7OTBrcMRjURr+DyxTz75JKqrq6NHjx45jzFx4sS0dnl5eaeZaLp16xY1NTX17YYTT64aHijzef07k0022SQeffTR+va0adOitrY2Kioq8hqn4YSWtOdjPvjggzF69Oi0W0pGfFU1evXVV8c3vvGNNoosmZ588smYNGlSfPrppzFlypTYb7/9Yr/99st7nIYXaJq7reTzzz8f//3f/92oSriysjIuuOCC2GefffKOoSNrON9UV1fH5MmTY7311st5jEmTJjV6vYcNG1aS+JKuW7duae1SzTcRbTfnpFKpeOqpp2LWrFkxe/bsmDVrVowcOTJ23333vMeaOnVqWnvQoEFp7c4yf5A7OXBx5MCtr7Mcx+TBuZMDtw9y4OLIgbOTA1MIeXDh5MCtr7Mcx+TAuZMDtx/y4OLIg7OTB7cORS4k2vDhw6OioqI+gaqtrY3XXnstvvnNb+Y8xsq3uIz46uDRu3fvksaZVAMGDIiFCxfWtz/++OOCxml4O7zO8BzbQjR8Zu2yZcvipZdeyjvRf+utt9LaSToRf/jhh+N//ud/oq6uLq1/zTXXjJtuuqmgv4xZY4014uijjy4onoULF8Y999yT1veDH/wg7Tve1s8Yvfbaa+Ptt9+ub/fq1augk5vJkyentRsmRisbP358xhObVVZZJcaOHRvbbLNN3vtvbUOHDi34c/HFF1/E3//+97S+hmOtvfbaae311lsv+vfvn/Z8zFdeeSWvE5uXX345rb3qqqu22fe3tV+/AQMGxHvvvVffLtV807Vr1zb7DpeVlcXZZ5+d9pn49NNP8z6xqaura/Ts5K222iqt3RnmD/IjBy6OHLj1dYbjmDw4P3LgwsiBiyMHLp4cmLYmDy6cHLj1dYbjmBw4P3LgwsmDiyMPLp48uP1R5EKi9e3bN0aMGJF2cvLoo4/mfGKTSqXiscceS+vbcccdSxpjkm200UZpk8s///nPWLp0aaMqy+b8+9//TmtvttlmpQivwxkxYkRUVVWl3bbx73//e14T06RJkxolBNttt12JIizOyy+/HKNGjWp0UjNs2LC4+eabC37e5Ne+9rU466yzCtp2ypQpjU5sfvrTn8Zaa61V0HgtYaONNko7uXn66adj/vz5jZ6XmM1HH33U6HPx9a9/PeO6kyZNiuOPP77Ric3qq68ef/jDH2L99dfPPfg2tPnmm8fmm29e0LYvvvhio8S8uc9YWVlZ7LjjjvHggw/W9z366KNx8MEH57zfhvPN9ttvH2VlZTlvX0qt/fpttNFG8eyzz9a3X3311Zg2bVrex4WG882mm27aZq9hRMTGG2+c9nO98MILef8V4ZNPPtnorxkaHtc7+vxB/uTAxZEDt76OfhyTB+dPDlwYOXBx5MClIQemLcmDCycHbn0d/TgmB86fHLhw8uDiyINLQx7cvpS3dQDQnL333jut/dBDD8XMmTNz2vbJJ59M+5KXlZXFAQccUMrwEm3nnXdOa8+fPz/++te/5jXG448/3qhyuOG4fKWioqLRrf/GjRsXH330Uc5j3HzzzWntPn36xPbbb1+S+Ioxc+bMOPXUU9NuexrxVfJ05513FnxS0xnsuuuuae0lS5bE3XffndcYV199dVq7rKwsvvOd7zRar7q6Ok466aRGz61ce+21409/+lO7OrFpCw3nm2effTbef//9nLZ9991347nnnkvrO/DAA0sWW9I1nBfq6uri1ltvzWuMiRMnpp1EZBq3tTXc/+LFi2PcuHE5b19TUxNjx45N61tjjTXi29/+dlpfR54/KJwcuHBy4NbXkY9j8uDCyIHbDzlw4eTAmcmBKZY8uDBy4NbXkY9jcuDCyIHbF3lw4eTBmcmDW5ciFxJvn332Sat0Xbx4ccZnQDY0bdq0GDNmTFrfjjvuGOuss06LxJlEu+22W6NbSv7ud7+LSZMm5bT9l19+Geedd15a3xprrFHQM+g6iyOPPDK6du1a366pqYlzzjmnUTV1Jk899VSjCfPggw9OxLNvf/WrXzW6oLDuuuvGzTff3OaPA0q6b33rW41O/G644Yacv4d/+tOf4pFHHknr23fffTP+hcKVV17ZKBHq379//PGPf4w111wzz8g7nx122CFtjqirq4uzzjqr0XNwG1q4cGGMGjUqUqlUfd8GG2zQqSqnR44c2eiZs7feemuj23Y2ZeHChXHGGWekvYY9e/aM73//+yWNM1977713o796u/rqqxvdSrMpF198caPnwf/4xz+OLl0a30yxo84fFE4OXDg5cNvoqMcxeXBh5MDthxy4cHLgzOTAFEseXBg5cNvoqMcxOXBh5MDtizy4cPLgzOTBrUuRC4nXu3fv+MlPfpLW9/TTT8eoUaOiuro64zZTpkyJY489Nr788sv6vvLy8jj99NNbNNak6dWrV5x00klpfYsXL47DDjssnnnmmazbTpw4MQ477LBGyeypp56a920uO5O11147jjrqqLS+1157LX70ox9l/auTxx9/PE455ZS0Sb2qqiqOO+64lgo1Zy+88EL84x//SOvr3r17XHfddVFVVdU2QbUjXbp0aXR7v8WLF8dPfvKTePfdd5vcrra2Nn7/+9/H+eefn9bfp0+fOO200xqtP3ny5LjzzjvT+srKyuLKK69s9MxMMisvL4+TTz45rW/ixIlxwgknxJw5czJuM3v27DjhhBPSnkEaEXHGGWe06a0VW1t5eXmceeaZaX11dXVx7LHHpt32M5PPPvssDj/88EYn5sccc0ybP/u7X79+jY7Dc+fOjWOOOSY+/PDDJrerrq6OX/ziF3HHHXek9W+55ZZx+OGHZ9ymI84fFEcOXDg5cNvoiMcxeXDh5MDthxy4cHLgdHJgSkUeXBg5cNvoiMcxOXDh5MDtizy4cPLgdPLgtlGWWvlVgISqqamJQw45JO15hhFfVQ8fddRRse2220ZVVVVMmzYtHn300bjrrrsa3abt+OOPj1NPPbUVo06GVCoVp512Wjz88MNp/WVlZbHddtvFQQcdFMOGDYvVVlstFi5cGO+99178/e9/j4cffrjRX0h8//vfj9/+9retGX6rmzJlSuyyyy5pfU888URez/WsqamJo446Ku35wRFfJaWHHXZY7LrrrrH22mvH0qVL4913342//vWvjU4cIr6qxt5zzz0L+0FK6Mc//nGjW+8NHTq06Gcab7/99rHDDjsUNUYp3q/Wctppp8X//d//pfV17do1DjzwwNhzzz1j/fXXj65du8b06dPjpZdeij/96U+NEr0uXbrETTfdlPFZ1Oeee27ce++9aX1rrLFG0Z+hzTffPPbaa6+ixmhtL774YhxxxBFpfQ1PPLI5/vjj48knn0zrGzRoUBx55JGx0047xYABA2LmzJnxr3/9K2677baYPn162rr77rtvXHrppYX/AG2smNfvkksuiVtuuaVR/xZbbBEHH3xwbLbZZrH66qvH0qVL46OPPorHH3887rvvvli2bFna+jvssEPceOONUVFRUfgPUiLLli2LQw89NN566620/srKyjjooINijz32iPXXXz8qKiriyy+/jGeeeSbuvvvu+OKLL9LWr6qqir/85S+x7rrrNrmvjjZ/UDw5cOHkwPmTBzcmDy6eHLj1yIGLIwdOJwemrcmDCyMHzp8cuDE5cPHkwK1LHlwceXA6eXD7ociFdmP69OlxxBFHNHouaC523333uPLKKxNxgGwLS5cujZ/97Gfxr3/9q+Axvvvd78Zll12WdvusjqhUifLixYvjhBNOiOeff76gOE4//fR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",
            "text/plain": [
              "<Figure size 2426.75x1500 with 15 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = load_data_for_model('olmoe')\n",
        "num_experts = df.Expert.nunique()\n",
        "k = 8\n",
        "df['Proportion'] *= k\n",
        "uniform_proportion = 1 / num_experts * k * 100\n",
        "\n",
        "plt.style.use('seaborn-v0_8-poster')\n",
        "plt.style.use('seaborn-v0_8-darkgrid')\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "sns.despine()\n",
        "\n",
        "COLORS = {\n",
        "    'GitHub': '#F0539B',\n",
        "    'arXiv': '#43C5E0',\n",
        "    'Wikipedia': '#B11BE8',\n",
        "    'Books': '#FDBE15',\n",
        "    'C4': '#2E3168',\n",
        "}\n",
        "\n",
        "FONTSIZE=35\n",
        "LABEL_FREQ = 8\n",
        "TICK_FREQ = 4\n",
        "PADDING = num_experts * 0.07\n",
        "\n",
        "g = sns.catplot(data=df, x='Expert', y='Proportion', row='Domain', hue=\"Domain\", col=\"Layer\", kind='bar',\n",
        "                row_order=COLORS.keys(), hue_order=COLORS.keys(), palette=COLORS.values(), order=range(num_experts),\n",
        "                aspect=2.5, height=3, gap=0.0, linewidth=0.0, width=1.0, margin_titles=True, sharex=False, saturation=0.9)\n",
        "\n",
        "\n",
        "labels = sum(([i]+[\"\"]*(LABEL_FREQ - 1) for i in range(0, num_experts, LABEL_FREQ)), [])\n",
        "g.set_xticklabels(labels)\n",
        "\n",
        "for rows in g.axes:\n",
        "    for ax in rows:\n",
        "        ax.axhline(y=uniform_proportion, color='grey', linestyle='--' , linewidth=3.88, alpha=0.7)\n",
        "        ax.grid(True, axis='y', linestyle='-', color='#FFE5B4', linewidth=2, alpha=0.7)\n",
        "        ax.tick_params(axis='x', which='major', length=4, width=1, color='black', direction='out', reset=True, bottom=True, top=False, labelsize=FONTSIZE)\n",
        "        ax.tick_params(axis='y', which='major', labelsize=FONTSIZE)\n",
        "\n",
        "        ticks = ax.get_xticks()\n",
        "        ticks = [t for t in ticks if t % TICK_FREQ == 0]\n",
        "        ax.set_xticks(ticks)\n",
        "\n",
        "    # Create a bit of padding\n",
        "    ax = rows[0]\n",
        "    ax.set_xlim(ax.get_xlim()[0], ax.get_xlim()[1]+PADDING)\n",
        "    ax = rows[1]\n",
        "    ax.set_xlim(ax.get_xlim()[0]-PADDING/2, ax.get_xlim()[1]+PADDING/2)\n",
        "    ax = rows[2]\n",
        "    ax.set_xlim(ax.get_xlim()[0]-PADDING, ax.get_xlim()[1])\n",
        "\n",
        "# Remove ticks for all but the bottom row\n",
        "for rows in g.axes[:-1]:\n",
        "    for ax in rows:\n",
        "        ax.set_xticklabels([])\n",
        "\n",
        "g.set_ylabels(\"\")\n",
        "g.set_xlabels(\"\")\n",
        "plt.subplots_adjust(hspace=0.6, wspace=0.0)\n",
        "g.figure.supylabel('Domain specialization (%)', fontsize=FONTSIZE, fontweight='bold', x=-0.025)\n",
        "g.figure.supxlabel('Expert ID', fontsize=FONTSIZE, fontweight='bold', y=-0.035, x=0.46)\n",
        "\n",
        "g.set_titles(row_template=\"\", col_template=\"Layer {col_name}\", size=FONTSIZE, fontweight=\"bold\")\n",
        "g.legend.set_title(\"\")\n",
        "sns.move_legend(g, \"upper center\", bbox_to_anchor=(0.45, 1.12), ncol=5, fontsize=FONTSIZE)\n",
        "\n",
        "plt.savefig('routing_olmoe_v2.pdf', bbox_inches='tight')\n",
        "plt.savefig('routing_olmoe_v2.jpg', bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1280x880 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2461.25x900 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dfs = []\n",
        "\n",
        "for model in ['OLMoE', 'OLMoE-SFT', 'OLMoE-DPO']:\n",
        "    df = load_data_for_model(model.lower())\n",
        "    num_experts = df.Expert.nunique()\n",
        "    k = 8\n",
        "    df['Proportion'] *= k\n",
        "    uniform_proportion = 1 / num_experts * k * 100\n",
        "\n",
        "    df[\"Model\"] = model\n",
        "    dfs.append(df)\n",
        "\n",
        "df = pd.concat(dfs, axis=0, ignore_index=True)\n",
        "df = df.query(\"Domain == 'Tulu-v3.1'\")\n",
        "\n",
        "plt.style.use('seaborn-v0_8-poster')\n",
        "plt.style.use('seaborn-v0_8-darkgrid')\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "sns.despine()\n",
        "\n",
        "COLORS = {\n",
        "    'OLMoE': '#2E3168',\n",
        "    'OLMoE-SFT': '#43C5E0',\n",
        "    'OLMoE-DPO': '#F0539B',\n",
        "}\n",
        "\n",
        "FONTSIZE=35\n",
        "LABEL_FREQ = 8\n",
        "TICK_FREQ = 4\n",
        "PADDING = num_experts * 0.07\n",
        "\n",
        "g = sns.catplot(data=df, x='Expert', y='Proportion', row='Model', hue=\"Model\", col=\"Layer\", kind='bar',\n",
        "                row_order=COLORS.keys(), hue_order=COLORS.keys(), palette=COLORS.values(), order=range(num_experts),\n",
        "                aspect=2.5, height=3, gap=0.0, linewidth=0.0, width=1.0, margin_titles=True, sharex=False, saturation=0.9)\n",
        "\n",
        "\n",
        "labels = sum(([i]+[\"\"]*(LABEL_FREQ - 1) for i in range(0, num_experts, LABEL_FREQ)), [])\n",
        "g.set_xticklabels(labels)\n",
        "\n",
        "for rows in g.axes:\n",
        "    for ax in rows:\n",
        "        ax.axhline(y=uniform_proportion, color='grey', linestyle='--' , linewidth=3.88, alpha=0.7)\n",
        "        ax.grid(True, axis='y', linestyle='-', color='#FFE5B4', linewidth=2, alpha=0.7)\n",
        "        ax.tick_params(axis='x', which='major', length=4, width=1, color='black', direction='out', reset=True, bottom=True, top=False, labelsize=FONTSIZE)\n",
        "        ax.tick_params(axis='y', which='major', labelsize=FONTSIZE)\n",
        "\n",
        "        ticks = ax.get_xticks()\n",
        "        ticks = [t for t in ticks if t % TICK_FREQ == 0]\n",
        "        ax.set_xticks(ticks)\n",
        "        ax.set_ylim(0, 100)\n",
        "\n",
        "\n",
        "    # Create a bit of padding\n",
        "    ax = rows[0]\n",
        "    ax.set_xlim(ax.get_xlim()[0], ax.get_xlim()[1]+PADDING)\n",
        "    ax = rows[1]\n",
        "    ax.set_xlim(ax.get_xlim()[0]-PADDING/2, ax.get_xlim()[1]+PADDING/2)\n",
        "    ax = rows[2]\n",
        "    ax.set_xlim(ax.get_xlim()[0]-PADDING, ax.get_xlim()[1])\n",
        "\n",
        "# Remove ticks for all but the bottom row\n",
        "for rows in g.axes[:-1]:\n",
        "    for ax in rows:\n",
        "        ax.set_xticklabels([])\n",
        "\n",
        "g.set_ylabels(\"\")\n",
        "g.set_xlabels(\"\")\n",
        "plt.subplots_adjust(hspace=0.6, wspace=0.0)\n",
        "g.figure.supylabel('Domain specialization (%)', fontsize=FONTSIZE, fontweight='bold', x=-0.025)\n",
        "g.figure.supxlabel('Expert ID', fontsize=FONTSIZE, fontweight='bold', y=-0.05, x=0.46)\n",
        "\n",
        "g.set_titles(row_template=\"\", col_template=\"Layer {col_name}\", size=FONTSIZE, fontweight=\"bold\")\n",
        "g.legend.set_title(\"\")\n",
        "sns.move_legend(g, \"upper center\", bbox_to_anchor=(0.45, 1.18), ncol=5, fontsize=FONTSIZE)\n",
        "\n",
        "plt.savefig('routing_tulu_v2.pdf', bbox_inches='tight')\n",
        "plt.savefig('routing_tulu_v2.jpg', bbox_inches='tight')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
